Shared Task on Understanding Figurative Language at FigLang2022

We are happy to announce a new shared task on Understanding Figurative Language as part of the Figurative Language Workshop (FigLang 2022) at EMNLP 2022. In recent years, there have been several benchmarks dedicated to figurative language understanding, which generally frame "understanding" as a recognizing textual entailment task -- deciding whether one sentence (premise) entails/contradicts another (hypothesis) (Chakrabarty et al 2021, Stowe et al 2022). We introduce a new shared task for figurative language understanding around this textual entailment paradigm, where the hypothesis is a sentence containing the figurative language expression (e.g., metaphor, sarcasm, idiom, simile) and the premise is a literal sentence containing the literal meaning. There are two important aspects of this task: 1) the task requires not only to generate the label (entail/contradict) but also to generate a plausible explanation for the prediction; 2) the entail/contradict label and the exploration are related to the meaning of the figurative language expression.

For more information about the shared task, including the link to the datasets, evaluation metrics and scripts important dates please visit the Shared task website (https://figlang2022sharedtask.github.io/). Participants can use the following CodaLab ( https://codalab.lisn.upsaclay.fr/competitions/5908) link to participate in the task as well as submit the predictions.

Important dates:

· July 10, 2022: CodaLab competition is open; training data can be downloaded

· Aug 15, 2022: Test data (available only to registered participants) can be downloaded and results submitted; performance will be tracked on CodaLab dashboard

· Aug 20, 2022: Last day for submitting predictions on test data

· Sept 7, 2022: Papers describing the systems are due

· Oct 9, 2022: Notification of acceptance

· TBD, 2022: Camera-ready papers due

· December 8, 2022: Workshop at EMNLP 2022

Organizing Team

Tuhin Chakrabarty, Columbia University; tuhin.chakr@cs.columbia.edu

Arkadiy Saakyan, Columbia University; as5423@columbia.edu

Debanjan Ghosh, Educational Testing Service; dghosh@ets.org

Smaranda Muresan, Data Science Institute, Columbia University;smara@columbia.edu